Traffic Signal Control (TSC) in urban areas is typically performed by (adaptive) cycle-based methods. These methods are characterized by their robustness and simplicity, but do not hold the potential to achieve optimal control. Model Predictive Control (MPC) is a control method that has proven to be a great solution for urban TSC in literature. MPC is a model-based control method that predicts and optimizes future states over a moving prediction horizon. This approach holds the potential to foresee events that disrupt traffic networks, and actively adjust signal behavior to anticipate these disruptions.

This thesis research investigates an MPC approach for urban TSC, where a bridge opening occurs multiple times a day at a busy junction in Leiden, Netherlands. A linear prediction model is used with linear inequality constraints and binary control inputs. Multiple scenarios are simulated, where the MPC controller is compared to a baseline controller developed in the COCON optimization tool. The first scenario is in regular traffic conditions, so no bridge opening. In other simulations, a 5-minute bridge opening is incorporated. Finally, an upstream intersection is included to manipulate upstream traffic flow.

The simulation results show a decrease in average delay time per road user of 20% for normal traffic conditions. When the bridge opening was included, a delay decrease of 25% was achieved. When the upstream intersection was included, a delay decrease of 41% was achieved, using a centralized approach.